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Industry: IT Finance Budgeting and Forecasting
Location: UK
Scope: Web
Client Overview:
Client built a Spend Analytics platform that offers a fast and real-time spend analytics platform to aid in making informed business decisions.
Goals/Challenges
- Establishing the QA process.
- Creating test cases from scratch, including Regression and Smoke suites, end-to-end testing, database testing, and API testing.
- Understanding business requirements.
- Automating the Power BI-based application, with a particular challenge in testing the graphs due to its graph-based nature.
Tools Used
- Azure DevOps (Management Tool)
- Microsoft Excel (for generating test cases)
- Power BI (for generating reports in graph format)
- Microsoft SQL Server (database)
- Postman (for manual API testing)
- Robot Framework (automation framework)
- Language: Python
- Selenium (for web automation)
- Sikuli (for Power BI graph automation)
- PyCharm (IDE)
Solutions
Phase 1:
- Requirement gathering and understanding.
- Application exploration.
- Establishing the QA process from scratch.
Phase 2:
- Defining the testing approach and strategy.
- Creating a test plan.
- Analyzing automation tools suitable for the application.
- Framework creation.
Phase 3:
- Writing test cases, both manual and automated.
- Performing functional/API testing and non-functional testing.
- Executing test cases manually and using automation.
Key Benefits:
- Improved overall product quality: Automation testing helps in detecting defects, bugs, and inconsistencies in the Power BI application more effectively.
- Reduction in manual efforts due to test script reusability: Automation testing reduces the dependency on manual testing efforts, as test scripts can be reused across different test cycles and scenarios.
- Graph Validation: The main challenge in testing the Power BI application was to validate the graphs and ensure their accuracy. Automation testing helped by capturing screenshots of the graphs using different tools and then comparing them against expected results. This automated approach ensured that the graphs were displayed correctly, data was represented accurately, and any deviations or issues were identified efficiently.
- Regression Testing: Automation testing allowed for the creation of regression test suites, which helped in quickly and reliably verifying the functionality of the Power BI application after any changes or updates.
- Continuous Integration and Deployment: Automation testing integrated seamlessly with CI/CD (Continuous Integration/Continuous Deployment) pipelines. By automating the execution of test cases within the CI/CD pipeline, it ensured that any changes or updates to the Power BI application were thoroughly tested before being deployed to production. This helped in maintaining the stability and reliability of the application, reducing the risk of introducing bugs or errors.
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